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Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease

Author

Listed:
  • Hao-Yun Kao

    (Department of Healthcare Administration and Medical Informatics, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Chi-Chang Chang

    (School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan
    Department of Information Management, Ming Chuan University, Taoyuan City 33300, Taiwan)

  • Chin-Fang Chang

    (Department of Otorhinolaryngology, Head and Neck Surgery, Jen-Ai Hospital, Taichung City 41222, Taiwan
    Cancer Medicine Center, Jen-Ai Hospital, Taichung City 41222, Taiwan
    Basic Medical Education Center, Central Taiwan University of Science and Technology, Taichung City 40601, Taiwan
    Department of Medical Education and Research, Jen-Ai Hospital, Taichung City 41222, Taiwan)

  • Ying-Chen Chen

    (School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan)

  • Chalong Cheewakriangkrai

    (Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Ya-Ling Tu

    (Center for General Education, National Taichung University of Science and Technology, Taichung City 40401, Taiwan)

Abstract

Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results ( p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.

Suggested Citation

  • Hao-Yun Kao & Chi-Chang Chang & Chin-Fang Chang & Ying-Chen Chen & Chalong Cheewakriangkrai & Ya-Ling Tu, 2022. "Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease," IJERPH, MDPI, vol. 19(3), pages 1-11, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1219-:d:730897
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    References listed on IDEAS

    as
    1. Chien-Lung Chan & Chi-Chang Chang, 2020. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 17(18), pages 1-7, September.
    2. Chin-Chuan Shih & Ssu-Han Chen & Gin-Den Chen & Chi-Chang Chang & Yu-Lin Shih, 2021. "Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme," IJERPH, MDPI, vol. 18(23), pages 1-13, December.
    3. Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, vol. 24(2), pages 225-232, May.
    4. Chi-Chang Chang & Chun-Chia Chen & Chalong Cheewakriangkrai & Ying Chen Chen & Shun-Fa Yang, 2021. "Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study," IJERPH, MDPI, vol. 18(17), pages 1-9, August.
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